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双功能预测模型开发：基于预训练模块与迁移学习的天然肽序列与SAFP修饰信息融合

news 2025/8/4 9:48:01

双功能预测模型开发：基于预训练模块与迁移学习的天然肽序列与SAFP修饰信息融合

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1. 项目概述与背景

1.1 项目背景

在生物医药领域，肽类药物因其高特异性、低毒性和良好的组织穿透性而受到广泛关注。神经修复相关多肽作为其中的一个重要分支，在治疗神经退行性疾病、神经损伤修复等方面展现出巨大潜力。然而，多肽药物的开发面临着诸多挑战，特别是如何准确预测其生物活性和修饰效应。

本项目旨在开发一个双功能预测模型，该模型能够：

基于预训练模块输出固定长度的特征向量（256维）
结合迁移学习技术，融合天然肽序列知识与SAFP（Structure-Activity-Function-Prediction）特有的修饰信息
为神经修复相关多肽的研究提供高效、准确的预测工具

1.2 技术挑战

开发此类模型面临的主要技术挑战包括：

如何有效表示肽序列的复杂特征
如何处理SAFP特有的修饰信息并将其与传统序列信息融合
在有限标注数据情况下实现高精度预测
确保模型的可解释性以满足生物医学研究的需求

1.3 项目时间线与现状

根据客户需求，项目计划在九月前完成。当前状态如下：

数据状态：神经修复相关的多肽数据尚未整理完毕
预算情况：暂无明确预算
技术基础：已有核心参考文章作为理论支持

2. 技术方案设计

2.1 整体架构

我们的双功能预测模型将采用以下架构：

输入层
│
├── 预训练模块 (输出256维特征向量)
│   ├── 序列编码层
│   ├── 注意力机制层
│   └── 特征提取层
│
└── SAFP修饰信息处理模块├── 修饰特征编码└── 修饰-序列关系建模
│
特征融合层
│
└── 双任务预测头├── 生物活性预测└── 修饰效应预测

2.2 预训练模块设计

预训练模块负责将可变长度的肽序列转换为固定长度的特征表示。我们将采用以下策略：

import torch
import torch.nn as nn
from transformers import BertModel, BertTokenizerclass PeptidePretrainModule(nn.Module):def __init__(self, hidden_dim=256):super().__init__()# 使用预训练的蛋白质语言模型作为基础self.backbone = BertModel.from_pretrained("Rostlab/prot_bert")# 适配层将BERT输出调整为256维self.adapter = nn.Sequential(nn.Linear(self.backbone.config.hidden_size, hidden_dim),nn.ReLU(),nn.LayerNorm(hidden_dim))def forward(self, peptide_sequences):# 输入肽序列的token IDs和attention maskoutputs = self.backbone(**peptide_sequences)# 取[CLS] token作为序列表示sequence_representation = outputs.last_hidden_state[:, 0, :]# 转换为256维特征return self.adapter(sequence_representation)

2.3 SAFP修饰信息处理模块

SAFP修饰信息需要特殊处理并与序列特征融合：

class SAFPModificationModule(nn.Module):def __init__(self, mod_feature_dim=64, hidden_dim=256):super().__init__()# 修饰类型嵌入层self.mod_type_embed = nn.Embedding(num_mod_types, mod_feature_dim)# 修饰位置处理self.position_encoder = PositionalEncoding(mod_feature_dim)# 修饰特征提取self.mod_processor = nn.Sequential(nn.Linear(mod_feature_dim, hidden_dim),nn.ReLU(),nn.LayerNorm(hidden_dim))# 修饰-序列交互注意力self.cross_attention = nn.MultiheadAttention(hidden_dim, num_heads=4)def forward(self, sequence_features, modification_info):# modification_info包含修饰类型、位置等mod_embeddings = self.mod_type_embed(modification_info['types'])mod_embeddings = self.position_encoder(mod_embeddings)mod_features = self.mod_processor(mod_embeddings)# 计算修饰特征与序列特征的交互attn_output, _ = self.cross_attention(query=sequence_features.unsqueeze(0),key=mod_features.unsqueeze(0),value=mod_features.unsqueeze(0))return attn_output.squeeze(0)

2.4 特征融合与多任务预测

class MultiTaskPredictor(nn.Module):def __init__(self, input_dim=256):super().__init__()# 共享的特征处理层self.shared_layers = nn.Sequential(nn.Linear(input_dim, input_dim),nn.ReLU(),nn.Dropout(0.2))# 生物活性预测头self.activity_head = nn.Sequential(nn.Linear(input_dim, input_dim//2),nn.ReLU(),nn.Linear(input_dim//2, 1))# 修饰效应预测头self.mod_effect_head = nn.Sequential(nn.Linear(input_dim, input_dim//2),nn.ReLU(),nn.Linear(input_dim//2, num_mod_effects))def forward(self, fused_features):shared_features = self.shared_layers(fused_features)activity = self.activity_head(shared_features)mod_effect = self.mod_effect_head(shared_features)return activity, mod_effect

3. 数据预处理与特征工程

3.1 肽序列数据处理

肽序列需要转换为适合模型输入的格式：

from Bio import SeqIO
from sklearn.preprocessing import LabelEncoder
import numpy as npclass PeptideSequenceProcessor:def __init__(self, max_length=50):self.tokenizer = BertTokenizer.from_pretrained("Rostlab/prot_bert", do_lower_case=False)self.max_length = max_lengthself.amino_acid_encoder = LabelEncoder()# 标准氨基酸+特殊tokenself.amino_acid_encoder.fit(['A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y','X','[CLS]','[SEP]','[PAD]'])def process_sequence(self, sequence):# 添加特殊tokensequence = f"[CLS]{sequence}[SEP]"# 使用ProtBERT的tokenizerencoded = self.tokenizer(sequence,max_length=self.max_length,padding='max_length',truncation=True,return_tensors='pt')return {'input_ids': encoded['input_ids'],'attention_mask': encoded['attention_mask']}

3.2 SAFP修饰信息处理

SAFP修饰信息需要标准化和编码：

class ModificationProcessor:def __init__(self):self.mod_type_encoder = LabelEncoder()self.position_encoder = PositionalEncoder()def process_modifications(self, modifications):"""modifications: 包含每个修饰的字典列表[{'type': 'phosphorylation', 'position': 15}, ...]"""# 编码修饰类型mod_types = [m['type'] for m in modifications]if not hasattr(self.mod_type_encoder, 'classes_'):self.mod_type_encoder.fit(mod_types)encoded_types = self.mod_type_encoder.transform(mod_types)# 处理修饰位置positions = [m['position'] for m in modifications]position_features = self.position_encoder.encode(positions)return {'types': torch.tensor(encoded_types, dtype=torch.long),'positions': torch.tensor(position_features, dtype=torch.float)}

3.3 数据增强策略

考虑到肽数据可能有限，我们需要实施数据增强：

class PeptideDataAugmenter:def __init__(self, mutation_rate=0.05):self.mutation_rate = mutation_rateself.amino_acids = ['A','C','D','E','F','G','H','I','K','L','M','N','P','Q','R','S','T','V','W','Y']def augment_sequence(self, sequence):seq_list = list(sequence)for i in range(len(seq_list)):if np.random.rand() < self.mutation_rate:# 随机替换氨基酸seq_list[i] = np.random.choice(self.amino_acids)return ''.join(seq_list)def augment_modifications(self, modifications, sequence_length):new_mods = []for mod in modifications:if np.random.rand() > 0.1:  # 90%概率保留原修饰new_mods.append(mod)# 10%概率添加随机修饰if np.random.rand() < 0.1:new_mods.append({'type': np.random.choice(self.mod_type_encoder.classes_),'position': np.random.randint(1, sequence_length+1)})return new_mods

4. 模型训练与优化

4.1 损失函数设计

多任务学习需要精心设计损失函数：

class MultiTaskLoss(nn.Module):def __init__(self, alpha=0.7):super().__init__()self.alpha = alpha  # 控制两个任务的权重self.regression_loss = nn.MSELoss()self.classification_loss = nn.CrossEntropyLoss()def forward(self, outputs, targets):activity_pred, mod_effect_pred = outputsactivity_true, mod_effect_true = targets# 生物活性预测损失 (回归)activity_loss = self.regression_loss(activity_pred, activity_true)# 修饰效应预测损失 (分类)mod_loss = self.classification_loss(mod_effect_pred, mod_effect_true)# 组合损失total_loss = self.alpha * activity_loss + (1 - self.alpha) * mod_lossreturn {'total': total_loss,'activity': activity_loss,'mod_effect': mod_loss}

4.2 训练流程

完整的训练流程实现：

class Trainer:def __init__(self, model, train_loader, val_loader, config):self.model = modelself.train_loader = train_loaderself.val_loader = val_loaderself.config = configself.optimizer = torch.optim.AdamW(model.parameters(),lr=config['lr'],weight_decay=config['weight_decay'])self.loss_fn = MultiTaskLoss(alpha=config['alpha'])self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(self.optimizer, 'min', patience=3)self.best_val_loss = float('inf')self.early_stop_counter = 0def train_epoch(self, epoch):self.model.train()total_loss = 0activity_metric = RegressionMetric()mod_effect_metric = ClassificationMetric()for batch in self.train_loader:self.optimizer.zero_grad()# 获取batch数据seq_data = batch['sequence']mod_data = batch['modification']activity_target = batch['activity']mod_effect_target = batch['mod_effect']# 前向传播seq_features = self.model.pretrain_module(seq_data)mod_features = self.model.safp_module(seq_features, mod_data)activity_pred, mod_effect_pred = self.model.predictor(mod_features)# 计算损失loss_dict = self.loss_fn((activity_pred, mod_effect_pred),(activity_target, mod_effect_target))# 反向传播loss_dict['total'].backward()nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)self.optimizer.step()# 记录指标total_loss += loss_dict['total'].item()activity_metric.update(activity_pred, activity_target)mod_effect_metric.update(mod_effect_pred, mod_effect_target)# 计算epoch指标avg_loss = total_loss / len(self.train_loader)activity_score = activity_metric.compute()mod_effect_score = mod_effect_metric.compute()return {'loss': avg_loss,'activity_r2': activity_score['r2'],'mod_effect_acc': mod_effect_score['accuracy']}def validate(self):self.model.eval()val_loss = 0activity_metric = RegressionMetric()mod_effect_metric = ClassificationMetric()with torch.no_grad():for batch in self.val_loader:# 获取batch数据seq_data = batch['sequence']mod_data = batch['modification']activity_target = batch['activity']mod_effect_target = batch['mod_effect']# 前向传播seq_features = self.model.pretrain_module(seq_data)mod_features = self.model.safp_module(seq_features, mod_data)activity_pred, mod_effect_pred = self.model.predictor(mod_features)# 计算损失loss_dict = self.loss_fn((activity_pred, mod_effect_pred),(activity_target, mod_effect_target))# 记录指标val_loss += loss_dict['total'].item()activity_metric.update(activity_pred, activity_target)mod_effect_metric.update(mod_effect_pred, mod_effect_target)avg_loss = val_loss / len(self.val_loader)activity_score = activity_metric.compute()mod_effect_score = mod_effect_metric.compute()# 学习率调整self.scheduler.step(avg_loss)# 早停检查if avg_loss < self.best_val_loss:self.best_val_loss = avg_lossself.early_stop_counter = 0# 保存最佳模型torch.save(self.model.state_dict(), 'best_model.pt')else:self.early_stop_counter += 1return {'val_loss': avg_loss,'val_activity_r2': activity_score['r2'],'val_mod_effect_acc': mod_effect_score['accuracy'],'early_stop': self.early_stop_counter >= self.config['patience']}def train(self):for epoch in range(self.config['epochs']):train_metrics = self.train_epoch(epoch)val_metrics = self.validate()# 打印日志print(f"Epoch {epoch+1}/{self.config['epochs']}")print(f"Train Loss: {train_metrics['loss']:.4f} | "f"Activity R2: {train_metrics['activity_r2']:.4f} | "f"Mod Effect Acc: {train_metrics['mod_effect_acc']:.4f}")print(f"Val Loss: {val_metrics['val_loss']:.4f} | "f"Val Activity R2: {val_metrics['val_activity_r2']:.4f} | "f"Val Mod Effect Acc: {val_metrics['val_mod_effect_acc']:.4f}")if val_metrics['early_stop']:print("Early stopping triggered")break

4.3 超参数优化

我们使用Optuna进行超参数优化：

import optunadef objective(trial):config = {'lr': trial.suggest_float('lr', 1e-5, 1e-3, log=True),'weight_decay': trial.suggest_float('weight_decay', 1e-6, 1e-3, log=True),'alpha': trial.suggest_float('alpha', 0.3, 0.9),'dropout': trial.suggest_float('dropout', 0.1, 0.5),'batch_size': trial.suggest_categorical('batch_size', [16, 32, 64]),'epochs': 50,'patience': 5}# 数据加载器train_loader, val_loader = get_data_loaders(config['batch_size'])# 初始化模型model = DualFunctionModel(pretrain_module=PeptidePretrainModule(),safp_module=SAFPModificationModule(),predictor=MultiTaskPredictor(dropout=config['dropout']))# 训练trainer = Trainer(model, train_loader, val_loader, config)trainer.train()# 返回验证损失作为优化目标return trainer.best_val_lossstudy = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=30)print("Best trial:")
trial = study.best_trial
print(f"Value: {trial.value}")
print("Params: ")
for key, value in trial.params.items():print(f"    {key}: {value}")

5. 模型评估与解释

5.1 评估指标

除了传统的准确率和损失值外，我们还实现了一系列生物信息学特定的评估指标：

class BioActivityMetrics:@staticmethoddef pearson_r(y_true, y_pred):# 计算Pearson相关系数x, y = y_true.numpy(), y_pred.numpy()return np.corrcoef(x, y)[0, 1]@staticmethoddef spearman_r(y_true, y_pred):# 计算Spearman秩相关系数from scipy import statsreturn stats.spearmanr(y_true.numpy(), y_pred.numpy()).correlation@staticmethoddef r2_score(y_true, y_pred):# 计算R²分数ss_res = torch.sum((y_true - y_pred)**2)ss_tot = torch.sum((y_true - torch.mean(y_true))**2)return 1 - ss_res / ss_totclass ModEffectMetrics:@staticmethoddef balanced_accuracy(y_true, y_pred):# 计算平衡准确率from sklearn.metrics import balanced_accuracy_scorereturn balanced_accuracy_score(y_true.numpy(), torch.argmax(y_pred, dim=1).numpy())@staticmethoddef matthews_corr(y_true, y_pred):# 计算Matthews相关系数from sklearn.metrics import matthews_corrcoefreturn matthews_corrcoef(y_true.numpy(),torch.argmax(y_pred, dim=1).numpy())

5.2 模型解释技术

为了增强模型的可解释性，我们实现了以下方法：

class ModelInterpreter:def __init__(self, model, tokenizer):self.model = modelself.tokenizer = tokenizerdef visualize_attention(self, sequence):# 获取注意力权重inputs = self.tokenizer(sequence, return_tensors='pt')outputs = self.model.pretrain_module.backbone(**inputs, output_attentions=True)# 可视化最后一层的注意力last_layer_attention = outputs.attentions[-1].mean(dim=1).squeeze()# 绘制热力图import matplotlib.pyplot as pltplt.figure(figsize=(10, 8))plt.imshow(last_layer_attention, cmap='viridis')plt.xticks(range(len(inputs.input_ids[0])), self.tokenizer.convert_ids_to_tokens(inputs.input_ids[0]))plt.yticks(range(len(inputs.input_ids[0])), self.tokenizer.convert_ids_to_tokens(inputs.input_ids[0]))plt.colorbar()plt.title("Attention Weights")plt.show()def feature_importance(self, sequence, modifications):# 使用Integrated Gradients计算特征重要性from captum.attr import IntegratedGradients# 准备输入inputs = self.tokenizer(sequence, return_tensors='pt')mod_info = self.mod_processor.process_modifications(modifications)# 定义前向函数def forward_func(input_ids, attention_mask):seq_data = {'input_ids': input_ids, 'attention_mask': attention_mask}seq_features = self.model.pretrain_module(seq_data)mod_features = self.model.safp_module(seq_features, mod_info)activity, _ = self.model.predictor(mod_features)return activity# 计算积分梯度ig = IntegratedGradients(forward_func)attributions = ig.attribute(inputs.input_ids,additional_forward_args=(inputs.attention_mask,),n_steps=50)# 可视化self._plot_importance(attributions, sequence)def _plot_importance(self, attributions, sequence):# 绘制特征重要性图import matplotlib.pyplot as plttokens = self.tokenizer.convert_ids_to_tokens(inputs.input_ids[0])attr_scores = attributions.mean(dim=2).squeeze().detach().numpy()plt.figure(figsize=(12, 4))plt.bar(range(len(tokens)), attr_scores)plt.xticks(range(len(tokens)), tokens, rotation=90)plt.title("Feature Importance Scores")plt.ylabel("Importance")plt.tight_layout()plt.show()

6. 部署与生产化

6.1 模型服务化

使用FastAPI创建模型服务：

from fastapi import FastAPI
from pydantic import BaseModel
import torchapp = FastAPI()# 加载训练好的模型
model = DualFunctionModel.load_from_checkpoint('best_model.pt')
model.eval()class PredictionRequest(BaseModel):sequence: strmodifications: listclass PredictionResponse(BaseModel):activity_prediction: floatmod_effect_prediction: strconfidence: float@app.post("/predict", response_model=PredictionResponse)
async def predict(request: PredictionRequest):# 预处理输入seq_processor = PeptideSequenceProcessor()mod_processor = ModificationProcessor()seq_data = seq_processor.process_sequence(request.sequence)mod_data = mod_processor.process_modifications(request.modifications)# 预测with torch.no_grad():seq_features = model.pretrain_module(seq_data)mod_features = model.safp_module(seq_features, mod_data)activity, mod_effect = model.predictor(mod_features)# 处理输出activity_value = activity.item()mod_effect_class = torch.argmax(mod_effect).item()mod_effect_prob = torch.softmax(mod_effect, dim=1).max().item()return {"activity_prediction": activity_value,"mod_effect_prediction": mod_effect_class,"confidence": mod_effect_prob}

6.2 性能优化

针对生产环境的性能优化：

class OptimizedModel(nn.Module):def __init__(self, original_model):super().__init__()self.model = original_model# 量化模型self.quantized = Falsedef quantize(self):# 动态量化self.model = torch.quantization.quantize_dynamic(self.model,{nn.Linear},dtype=torch.qint8)self.quantized = Truedef optimize_for_inference(self):# 使用TorchScript优化if not self.quantized:self.quantize()# 转换为TorchScriptexample_input = self._get_example_input()self.model = torch.jit.trace(self.model, example_input)torch.jit.freeze(self.model)def _get_example_input(self):# 返回一个示例输入用于追踪return ({'input_ids': torch.randint(0, 30, (1, 50)),'attention_mask': torch.ones((1, 50))},{'types': torch.tensor([0, 1]),'positions': torch.tensor([[0.1], [0.2]])})def forward(self, seq_data, mod_data):return self.model(seq_data, mod_data)